This paper considers specular reflection multipath exploitation by jointly designing the transmit waveform and receive filter for a colocated multiple-input-multiple-output (MIMO) radar system. The goal is to maximize the output signal-to-interference-pulse-noise ratio (SINR) at the receiver with a constant modulus constraint (CMC) on the transmit waveform. Assuming the multipath information to be known or partially known, we consider the waveform-filter design under two different cases. In the first case, the multipath information is exactly known. We devise Algorithm 1 to optimize the transmit waveform and receive filter alternatively. In the second case, the phase information of multipath reflection is unavailable. Under this situation, the minimax or robust waveform-filter design model is established; i.e., we maximize the worst-case SINR against the phase uncertainties. Algorithm 2 is proposed to solve the corresponding minimax optimization problem through alternating iterations. At each iteration, the closed form of optimal receive filter is derived, and waveform optimization is converted to a convex optimization problem through semi-definite relaxation (SDR). Finally, numerical experiments are carried out to verify the effectiveness of the proposed algorithms. The Results demonstrate the superiority of the designed waveform-filter pair compared with some existing solutions.

In modern electronic warfare, active jamming is an important way to prevent the target from being detected by the radar sensors. This paper considers the problem of designing effective jamming signals with limited jamming power. By taking the relative entropy as the figure of merit, we formulate the jamming signal design as a matrix optimization problem which is Non-Polynomial (NP) hard in general. To solve the resultant problem, we conceive an iterative algorithm, named by Relative Entropy Jamming Optimization Algorithm (REJOA), based on combining the Majorization Minimization (MM) technique and the matrix factorization together. The conceived algorithm updates the optimization variable in a closed form (or semi-closed form) at each iteration, and guarantees theoretical convergence. Finally, we compare our design with the Mutual Information (MI) based design and the Signal to Jamming plus Noise Ratio (SJNR) based design through numerical experiments. Results highlight that, compared with the state-of-the-art designs, our design achieves better jamming performance with the same jamming power.

We address radar waveform design problem for extended targets under the Peak-to-Average Ratio (PAR) constraint. Based on the stochastic model of the Target Impulse Response (TIR), the distribution of output Signal-to-Interference-pulse-Noise Ratio (SINR) is derived. After that, a novel waveform design metric, named Probabilistically Robust Detection (PRD), is proposed to improve the stability of detection performance against the stochastic TIR. The geometric and limit interpretations for the PRD metric are given to show its rationality. Leveraging on the Semi-Definite Relaxation schemes and the Dinkelbach's algorithm, we devise algorithms to optimize waveform covariance matrix based on the proposed metric in high and low SINR situations, respectively. It is theoretically proved that the global optimal waveform covariance matrix can be achieved in high SINR situations. On the contrary, only the sub-optimal waveform covariance matrix can be obtained in low SINR situations. The transmit waveform is synthesized from the optimized waveform covariance matrix through randomization schemes. Numerical experiments are carried out to evaluate the PRD metric as well as the proposed algorithms. Results verify the effectiveness of the proposed algorithms. Compared with the current design metric, the proposed PRD metric exhibits strong probabilistic robustness on detecting extended targets.

This correspondence considers the problem of robust radar waveform design against target interpulse fluctuation with constraints on the transmit energy, similarity, and signal dynamic range. We aim to make robust the transmit waveform and receive filter with respect to the Target Amplitude Vector (TAV) uncertainties by enhancing the worst-case Signal-to-Noise-Ratio (SNR) over the modeled ellipsoidal set. First of all, the filter is directly solved leveraging on the existence of saddle point condition, which leads to a suitable simplification of the original non-convex problem. Then, to handle the resultant max-min problem, the Sequential Lagrange Dual Approximation (SLDA) procedure is devised. Specifically, we update the transmit waveform via solving a sequence of max-min approximate problems, which are proven to be hidden convex relying on the theory of Lagrange duality. Finally, the filter is synthesized after solving a Quadratical Constraint Quadratic Programming (QCQP) problem based on the optimized waveform. Numerical results highlight the robustness of the proposed design which guarantees an enhanced worst-case SNR with respect to TAV uncertainties.

This paper considers the radar transceiver design problem for extended targets, aiming at improving the detection performance of radar systems. To circumvent the complicated Neyman-Pearson (NP) detector, we adopt the detector with linear structure. By analyzing its detection performance, we construct the transceiver design criterion. Based on this criterion, the waveform-filter design is formulated as a highly non-convex fractional programming problem with Constant Modulus Constraints (CMC) on the waveform. To tackle the formulated problem, we develop a Block Successive Upper-bound Minimization (BSUM) framework based algorithm, in which the transceiver optimization variables are divided into blocks. It is shown that the divided blocks can be updated in a closed form at each iteration without the help of external optimization toolbox. The convergence of the proposed algorithm is proved theoretically. Numerical experiments validate the effectiveness of the proposed algorithm. Results highlight that, the proposed criterion achieves comparable performance to the state-of-the-art criteria on the NP detector, but outperforms its counterparts on the linear detector.

We address the robust waveform-filter design problem for extended targets with a colocated Multiple-Input Multiple-Output (MIMO) radar . The goal is to maximize the worst-case Signal-to-Interference-pulse-Noise Ratio (SINR) at the receiver against the uncertain Target Impulse Response (TIR) with the Peak-to-Average Ratio (PAR) constraint imposed on the waveform, which results in a non-convex minimax optimization problem. Two kinds of uncertainty sets for the TIR, namely, the spherical set and the annular set, are considered. Combing the duality theory in optimization and the Semi-Definite Relaxation (SDR) technique, we devise the Lagrangian Duality Semi-Definite Relaxation (LDSDR) and the Lagrangian Duality Double SemiDefinite Relaxation (LDDSDR) algorithms to tackle the associated waveform-filter design problems against the spherical and the annular sets, respectively. The convergences of the proposed algorithms are proved theoretically. Numerical results verify the effectiveness of the proposed algorithms. Compared with the current algorithms for the spherical set, the proposed LDSDR algorithm achieves the highest worst-case SINR with a reduced computational complexity